Pavement Missing Condition Data Imputation through Collective Learning-Based Graph Neural Networks

TL;DR

This paper introduces a novel collective learning-based graph neural network method for imputing missing pavement condition data, leveraging adjacency and dependency information to improve data completeness and accuracy.

Contribution

The paper presents a new graph neural network model that effectively captures dependencies between pavement sections for missing data imputation, outperforming existing methods.

Findings

Model accurately imputes missing pavement data.

Captures dependencies between adjacent sections.

Demonstrates promising results on real data.

Abstract

Pavement condition data is important in providing information regarding the current state of the road network and in determining the needs of maintenance and rehabilitation treatments. However, the condition data is often incomplete due to various reasons such as sensor errors and non-periodic inspection schedules. Missing data, especially data missing systematically, presents loss of information, reduces statistical power, and introduces biased assessment. Existing methods in dealing with missing data usually discard entire data points with missing values or impute through data correlation. In this paper, we used a collective learning-based Graph Convolutional Networks, which integrates both features of adjacent sections and dependencies between observed section conditions to learn missing condition values. Unlike other variants of graph neural networks, the proposed approach is able to capture dependent relationship between the conditions of adjacent pavement sections. In the case study, pavement condition data collected from Texas Department of Transportation Austin District were used. Experiments show that the proposed model was able to produce promising results in imputing the missing data.